Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding

This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99\% in identifying prime numbers and 79\% for non-prime numbers from an inherently imbalanced sequential series of integers, while exhibiting rapid model convergence before …

architectures classification convergence core cs.lg development encoding intersection machine machine learning network neural network novel numbers paper prime rate recall research theory